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Journal articles on the topic 'Cognition Mathematical models'
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Wang, Yingxu. "On the Mathematical Theories and Cognitive Foundations of Information." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 3 (July 2015): 42–64. http://dx.doi.org/10.4018/ijcini.2015070103.
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A recent discovery in computer and software sciences is that information in general is a deterministic abstract quantity rather than a probability-based property of the nature. Information is a general form of abstract objects represented by symbolical, mathematical, communication, computing, and cognitive systems. Therefore, information science is one of the contemporary scientific disciplines collectively known as abstract sciences such as system, information, cybernetics, cognition, knowledge, and intelligence sciences. This paper presents the cognitive foundations, mathematical models, and formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.
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Sommer, Friedrich T., and Pentti Kanerva. "Can neural models of cognition benefit from the advantages of connectionism?" Behavioral and Brain Sciences 29, no. 1 (February 2006): 86–87. http://dx.doi.org/10.1017/s0140525x06379022.
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Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that is, the high representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced representations of combinatorial structures.
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Frischkorn, Gidon, and Anna-Lena Schubert. "Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application." Journal of Intelligence 6, no. 3 (July 17, 2018): 34. http://dx.doi.org/10.3390/jintelligence6030034.
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Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.
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Moustafa, Ahmed A., Angela Porter, and Ahmed M. Megreya. "Mathematics anxiety and cognition: an integrated neural network model." Reviews in the Neurosciences 31, no. 3 (April 28, 2020): 287–96. http://dx.doi.org/10.1515/revneuro-2019-0068.
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AbstractMany students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.
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Wagner, Roy. "Cognitive stories and the image of mathematics." THEORIA. An International Journal for Theory, History and Foundations of Science 33, no. 2 (June 20, 2018): 305. http://dx.doi.org/10.1387/theoria.17917.
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Аникеева, Ольга, and Olga Anikyeyeva. "Development of Socio-Historical Models as a Cognitive Process: A Cross-Disciplinary Analysis." Servis Plus 8, no. 2 (June 3, 2014): 4–9. http://dx.doi.org/10.12737/3886.
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The article analyses the problems of modeling as a means of socio-historical cognition. The major discrepancy lies in the fact that the practice of cognition, as well as change-oriented activity, frequently
employ modeling, while the principles and methods of model-development have not been clearly defined. The article considers the correlation between modeling and the conventional methods of historical research, and identifies the common and specific aspects of their implementation, the peculiarities of socio-historical modeling and its Junctions.
Modern science regards a model as analogous to a protoimage (a fact, an event, a process), its symbolic representation, or an idealized pattern (actions, behavior). The article highlights the basic principles underlying the development of socio-historical models: a model is representational (reflecting the ontologicalfeatures of the protoimage), relevant, both conditional and autonomous, moreover, a model has its individual life cycle, with the existence and development of the model determined by its cognitive value.
Modeling as a cognitive method emerged in response to the new perspective which viewed socio-historical processes as products of the meaningful activity of the agent. One of the most significant constituents of the model is its axiological motivation, which reflects the axiological system and the ideology immanent to the protoimage, and, thus, accounts for both the specifics and the essence of modeling. Another peculiarity and forte of modeling is the possibility and tolerance of the quantification of socio-historical processes, that is, translating qualitative characteristics onto the quantitative plane and developing mathematical models which lend themselves to mathematical study and interpretation, reducing ideological and axiological influences.
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RAY, ASOK, SHASHI PHOHA, and SOUMIK SARKAR. "BEHAVIOR PREDICTION FOR DECISION AND CONTROL IN COGNITIVE AUTONOMOUS SYSTEMS." New Mathematics and Natural Computation 09, no. 03 (October 3, 2013): 263–71. http://dx.doi.org/10.1142/s1793005713400061.
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This paper presents an innovative concept of behavior prediction for decision and control in cognitive autonomous systems. The objective is to coordinate human–machine collaboration such that human operators can assess and enable autonomous systems to utilize their experiential and unmodeled domain knowledge and perception for mission execution. The concept of quantum probability is proposed to construct a unified mathematical framework for interfacing between models of human cognition and machine intelligence.
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Broekaert, Jan, Irina Basieva, Pawel Blasiak, and Emmanuel M. Pothos. "Quantum-like dynamics applied to cognition: a consideration of available options." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2106 (October 2, 2017): 20160387. http://dx.doi.org/10.1098/rsta.2016.0387.
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Quantum probability theory (QPT) has provided a novel, rich mathematical framework for cognitive modelling, especially for situations which appear paradoxical from classical perspectives. This work concerns the dynamical aspects of QPT, as relevant to cognitive modelling. We aspire to shed light on how the mind's driving potentials (encoded in Hamiltonian and Lindbladian operators) impact the evolution of a mental state. Some existing QPT cognitive models do employ dynamical aspects when considering how a mental state changes with time, but it is often the case that several simplifying assumptions are introduced. What kind of modelling flexibility does QPT dynamics offer without any simplifying assumptions and is it likely that such flexibility will be relevant in cognitive modelling? We consider a series of nested QPT dynamical models, constructed with a view to accommodate results from a simple, hypothetical experimental paradigm on decision-making. We consider Hamiltonians more complex than the ones which have traditionally been employed with a view to explore the putative explanatory value of this additional complexity. We then proceed to compare simple models with extensions regarding both the initial state (e.g. a mixed state with a specific orthogonal decomposition; a general mixed state) and the dynamics (by introducing Hamiltonians which destroy the separability of the initial structure and by considering an open-system extension). We illustrate the relations between these models mathematically and numerically. This article is part of the themed issue ‘Second quantum revolution: foundational questions’.
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GOPISETTI, NAGA-SAI-RAM, MARIA LEONILDE ROCHA VARELA, and JOSE MACHADO. "HUMAN COGNITION INSPIRED PROCEDURES FOR PART FAMILY FORMATION BASED ON NOVEL INSPECTION BASED CLUSTERING APPROACH." DYNA 96, no. 5 (September 1, 2021): 546–52. http://dx.doi.org/10.6036/9997.
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Human cognition based procedures are promising approaches for solving different kind of problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to part machine grouping, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics. Keywords: Cellular manufacturing systems; part family formation; human cognition; inspection-based clustering.
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Reihenova, Austra. "MODELLING OF MATHEMATICAL PROCESSES AS A SCIENTIFIC COGNITION IN HIGH SCHOOL." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 3 (May 20, 2020): 516. http://dx.doi.org/10.17770/sie2020vol3.5016.
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The topicality of the article relates to the use of modelling in a real, complicated and complex process, with the need to forecast the progress and results of the occurrence. Article problem: In school, the focus is on building theoretical models, without real-life context. In real life, the problems are interdisciplinary, more difficult to define than in the theoretical model. The student should be able to transfer knowledge and concepts from one learning discipline in which he can deal with the problem to another. Mathematical modelling offers opportunities to connect and use knowledge from different disciplines. The aim of the article is to stimulate interest in the use of diverse learning approaches and forms, on the learning of mathematics as science, on its application in other scientific disciplines to address problems, on mathematics as a form of systemic thinking and on mathematical modelling as a learning method. The study used student test papers and open-ended questionnaires to collect data. The research used data triangulation method for data processing.
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Estrada-Rojo, Francisco, Ricardo Jesús Martínez-Tapia, Francisco Estrada-Bernal, Marina Martínez-Vargas, Adán Perez-Arredondo, Luis Flores-Avalos, and Luz Navarro. "Models used in the study of traumatic brain injury." Reviews in the Neurosciences 29, no. 2 (February 23, 2018): 139–49. http://dx.doi.org/10.1515/revneuro-2017-0028.
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AbstractTraumatic brain injury (TBI) is a contemporary health problem and a leading cause of mortality and morbidity worldwide. Survivors of TBI frequently experience disabling long-term changes in cognition, sensorimotor function, and personality. A crucial step in understanding TBI and providing better treatment has been the use of models to mimic the event under controlled conditions. Here, we describe the known head injury models, which can be classified as whole animal (in vivo),in vitro, and mathematical models. We will also review the ways in which these models have advanced the knowledge of TBI.
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Baltieri, Manuel, and Christopher Buckley. "PID Control as a Process of Active Inference with Linear Generative Models." Entropy 21, no. 3 (March 7, 2019): 257. http://dx.doi.org/10.3390/e21030257.
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In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.
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D’Alessandro, Marco, Giuseppe Gallitto, Antonino Greco, and Luigi Lombardi. "A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data." Brain Sciences 10, no. 3 (March 1, 2020): 138. http://dx.doi.org/10.3390/brainsci10030138.
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Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.
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Grabauskienė, Vaiva, and Oksana Mockaitytė-Rastenienė. "AN EXPRESSION OF MATHEMATICAL CONNECTIONS IN MULTIPLICATION-RELATED THINKING IN THIRD AND FOURTH GRADES OF PRIMARY SCHOOL." ŠVIETIMAS: POLITIKA, VADYBA, KOKYBĖ / EDUCATION POLICY, MANAGEMENT AND QUALITY 11, no. 1 (August 25, 2019): 9–29. http://dx.doi.org/10.48127/spvk-epmq/19.11.09.
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Mathematical comprehension is closely related to a cognition of mathematical connections. A multiplication is a mathematical operation characterized by complex mathematical connections. Students are early introduced with the multiplication. Therefore, in primary school, not so developed cognition of mathematical connections may become a reason for difficulties in Maths. A functionality of concept is based on a view to a multiplication. The analysis scientific literature revealed that a thinking of multiplication can be either additive or multiplicative. Additionally, the multiplication learning has a variety of additive and multiplicative explanations. Because they use different specificity of visualization, the models are not equally suitable for teaching children about different properties of multiplication. Based on research, in Math classes, students are only introduced with few of the models, not covering a whole variety of them. In the research, a paper and pencil type of survey consisted of 157 participants from 3rd and 4th Grades, eight different classes from four different schools. The students had to fill the table explaining multiplication of 5 x 12 in a form of writing and drawing. The quantitative analysis of results has showed that in Grades 3 to 4, the additive view to multiplication is much more prevalent, in comparison to the multiplicative reasoning. The array model is used often but not in an extensive way. The students do not know other types of multiplicative type models. In conclusion, the results showed that students of Grades 3rd and 4th knew not enough about the mathematical connections. Therefore, teachers should pay more attention to teaching students various ways of visualizing, for children, to obtain a comprehensive understanding of the multiplication process. Acknowledgement. This work was supported by a grant (No. 09.2.1-ESFA-K-728-01-0040) from the ESFA. Keywords: additive reasoning, multiplication learning, multiplicative reasoning, primary mathematics education.
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Afraimovich, Valentin S., Todd R. Young, and Mikhail I. Rabinovich. "Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking." International Journal of Bifurcation and Chaos 24, no. 10 (October 2014): 1450132. http://dx.doi.org/10.1142/s0218127414501326.
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Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transient dynamics. The mathematical image for a robust transient is a stable heteroclinic channel consisting of a chain of saddles connected by unstable separatrices. We focus here on hierarchical chunking dynamics that can represent several cognitive activities. Chunking is the dynamical phenomenon that means dividing a long information chain into shorter items. Chunking is known to be important in many processes of perception, learning, memory and cognition. We prove that in the phase space of the model that describes chunking there exists a new mathematical object — heteroclinic sequence of heteroclinic cycles — using the technique of slow–fast approximations. This new object serves as a skeleton of motions reflecting sequential features of hierarchical chunking dynamics and is an adequate image of the chunking processing.
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Kornbrot, Diana. "The Utility of Categorising Multidimensional Mathematical Models in Psychology. Review of Multidimensional Models of Perception and Cognition, by F. Gregory Ashby." Journal of Mathematical Psychology 38, no. 3 (September 1994): 392–406. http://dx.doi.org/10.1006/jmps.1994.1028.
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Toulopoulou, T., X. Zhang, S. Cherny, R. Straub, K. Berman, D. Dickinson, P. Sham, and D. Weinberger. "Polygenic risk profile score increases schizophrenia liability mostly through cognition pathways: mathematical causation models with polygenic risk." European Neuropsychopharmacology 27 (October 2017): S885—S886. http://dx.doi.org/10.1016/s0924-977x(17)31582-1.
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Temperley, David. "Probabilistic Models of Melodic Interval." Music Perception 32, no. 1 (September 1, 2014): 85–99. http://dx.doi.org/10.1525/mp.2014.32.1.85.
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Two probabilistic models of melodic interval are compared. In the Markov model, the “interval probability” of a note is defined by the corpus frequency of its melodic interval (the interval to the previous note), conditioned on the previous one or two intervals; in the Gaussian model, the interval probability is a simple mathematical function of the size of the note’s melodic interval and its position in relation to the range of the melody. In both models, this interval probability is then multiplied by the probability of the note’s scale degree to yield its actual probability. The two models were tested on four corpora of tonal melodies using cross-entropy. The Markov model yielded a somewhat lower (better) cross-entropy than the Gaussian model, but is also much more complex, requiring far more parameters. The models were also tested on melodic expectation data, and on their ability to predict the distribution of intervals in a corpus. Possible ways of improving the models are discussed, as well as their broader implications for music cognition.
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Cobb, Paul. "Review: A Double-Edged Sword." Journal for Research in Mathematics Education 20, no. 2 (March 1989): 213–18. http://dx.doi.org/10.5951/jresematheduc.20.2.0213.
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The notion of intuition frequently crops up in accounts of mathematical experiences (e.g., Davis & Hersh, 1981), and we have an intuitive idea of what is meant. As Fischbein notes, “intuition is generally seen as a primary phenomenon which may be described but which is not reducible to more elementary components” (p. ix). To rectify this situation, Fischbein presents a theory of mathematical and scientific intuition. In doing so, he synthesizes empirical research on problem solving, images and models, beliefs, and developmental stages of intelligence, drawing on examples from the history of science and mathematics. The book is marked by a masterly display of scholarship and makes a fundamental contribution to the analysis of mathematical cognition.
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Salimpoor, Valorie N., Catie Chang, and Vinod Menon. "Neural Basis of Repetition Priming during Mathematical Cognition: Repetition Suppression or Repetition Enhancement?" Journal of Cognitive Neuroscience 22, no. 4 (April 2010): 790–805. http://dx.doi.org/10.1162/jocn.2009.21234.
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We investigated the neural basis of repetition priming (RP) during mathematical cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning as an alternate model for behavioral facilitation. We examined the neural basis of RP during mathematical problem solving in the context of these two models of learning. Brain imaging and behavioral data were acquired from 39 adults during novel and repeated presentation of three-operand mathematical equations. Despite wide-spread decreases in activation during repeat, compared with novel trials, there was no direct relation between behavioral facilitation and the degree of repetition suppression in any brain region. Rather, RT improvements were directly correlated with repetition enhancement in the hippocampus and the posteromedial cortex [posterior cingulate cortex, precuneus, and retrosplenial cortex; Brodmann's areas (BAs) 23, 7, and 30, respectively], regions known to support memory formation and retrieval, and in the SMA (BA 6) and the dorsal midcingulate (“motor cingulate”) cortex (BA 24d), regions known to be important for motor learning. Furthermore, improvements in RT were also correlated with increased functional connectivity of the hippocampus with both the SMA and the dorsal midcingulate cortex. Our findings provide novel support for the hypothesis that repetition enhancement and associated stimulus-response learning may facilitate behavioral performance during problem solving.
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Jones, Matt, and Bradley C. Love. "Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition." Behavioral and Brain Sciences 34, no. 4 (August 2011): 169–88. http://dx.doi.org/10.1017/s0140525x10003134.
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AbstractThe prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.
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Perlovsky, Leonid. "Language and Cognition Interaction Neural Mechanisms." Computational Intelligence and Neuroscience 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/454587.
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How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a “teacher.” A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's “language prewired brain” built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures.
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Mark, D. M., and A. U. Frank. "Experiential and Formal Models of Geographic Space." Environment and Planning B: Planning and Design 23, no. 1 (February 1996): 3–24. http://dx.doi.org/10.1068/b230003.
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In this paper human experience and perception of phenomena and relations in space are studied. This focus is in contrast to previous work where space and spatial relations were examined as objective phenomena of the world. This study leads in turn to a goal: to identify models of space that can be used both in cognitive science and in the design and implementation of geographic information systems (GISs). Experiential models of the world are based on sensorimotor and visual experiences with environments, and form in individual minds, as the associated bodies and senses experience their worlds. Formal models consist of axioms expressed in a formal language, together with mathematical rules to infer conclusions from these axioms. In this paper we will review both types of models, considering each to be an abstraction of the same ‘real world’. The review of experiential models is based primarily on recent developments in cognitive science, expounded by Rosch, Johnson, Talmy, and especially Lakoff. In these models it is suggested that perception and cognition are driven by image-schemata and other mental models, often language-based. Cross-cultural variations are admitted and even emphasized. The ways in which people interact with small-scale (‘tabletop’) spaces filled with everyday objects are in sharp contrast to the ways in which they experience geographic (large-scale) spaces during wayfinding and other spatial activities. We then address the issue of the ‘objective’ geometry of geographic space. If objectivity is defined by measurement, this leads to a surveyor's view and a near-Euclidean geometry. These models are then related to issues in the design of GISs. To be implemented on digital computers, geometric concepts and models must be formalized. The idea of a formal geometry of natural language is discussed and some aspects of it are presented. Formalizing the links between cognitive categories and models on the one hand and between geometry and computer representations on the other are key elements in the research agenda.
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Raghubar, Kimberly P., Marcia A. Barnes, Mary Prasad, Chad P. Johnson, and Linda Ewing-Cobbs. "Mathematical Outcomes and Working Memory in Children With TBI and Orthopedic Injury." Journal of the International Neuropsychological Society 19, no. 3 (November 20, 2012): 254–63. http://dx.doi.org/10.1017/s1355617712001312.
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AbstractThis study compared mathematical outcomes in children with predominantly moderate to severe traumatic brain injury (TBI;n= 50) or orthopedic injury (OI;n=47) at 2 and 24 months post-injury. Working memory and its contribution to math outcomes at 24 months post-injury was also examined. Participants were administered an experimental cognitive addition task and standardized measures of calculation, math fluency, and applied problems; as well as experimental measures of verbal and visual-spatial working memory. Although children with TBI did not have deficits in foundational math fact retrieval, they performed more poorly than OIs on standardized measures of math. In the TBI group, performance on standardized measures was predicted by age at injury, socioeconomic status, and the duration of impaired consciousness. Children with TBI showed impairments on verbal, but not visual working memory relative to children with OI. Verbal working memory mediated group differences on math calculations and applied problems at 24 months post-injury. Children with TBI have difficulties in mathematics, but do not have deficits in math fact retrieval, a signature deficit of math disabilities. Results are discussed with reference to models of mathematical cognition and disability and the role of working memory in math learning and performance for children with TBI. (JINS, 2013,19, 1–10)
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Schubert, Anna-Lena, and Gidon T. Frischkorn. "Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences." Current Directions in Psychological Science 29, no. 2 (February 13, 2020): 140–46. http://dx.doi.org/10.1177/0963721419896365.
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More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.
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Gao, Yuan. "Rethinking the Formalism-Substantivism Debate in Social Science: A Perspective from Recent Developments in Economic Methodology." Modern China 47, no. 1 (May 28, 2020): 3–25. http://dx.doi.org/10.1177/0097700420924603.
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Taking as its point of departure the 1960s formalism-substantivism debate in social science methodology, this article argues that what is distinctive about the new development of formalism in economics since then is mainly the prevalence of using “complete models”—tractable, manipulable, and fully specified mathematical objects—to construct and express theories. The objective of complete models is not to establish general laws, but to formulate auxiliary devices of cognition to facilitate the explanation of targeted aspects of the empirical world; not to create idealistic or ideological discourses, but to derive implications with empirically delimited utility—this in order to make inferences that cannot be achieved via purely qualitative methods. This methodological trend is to some extent a substantivization of formalist economics. Exploring its nature can help clarify the unique cognitive value of contemporary formalism and answer the question of why substantivism is still an irreplaceable approach to social scientific studies, even in an age dominated by formalism.
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Studenova, Tamara Iurevna. "Evolution of approaches to the organization of the education system." Moscow University Pedagogical Education Bulletin, no. 2 (June 29, 2016): 53–66. http://dx.doi.org/10.51314/2073-2635-2016-2-53-66.
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Given are the principles of building teaching models when the genetic approach to the formation of mathematical concepts is employed, as well as the classification of teaching models founded on the historical stages of cognition development. The genetic approach to the formation of concepts has been developed and discussed in the works of our leading psychologists V.V. Davidov, D.B. Elconin, P.Y. Galperin, L.F. Oвukhova and others. The given study in accordance with the above mentioned theoretical assumptions tackles the formation of concepts from a psychosemeiotic point of view. This approach makes it possible to find other principles, stages and methods of teaching. The article is intended for educationists, didactors, methods teachers, school psychologists and teachers.
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Burgin, Mark. "Triadic Structures in Interpersonal Communication." Information 9, no. 11 (November 16, 2018): 283. http://dx.doi.org/10.3390/info9110283.
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Communication, which is information exchange between systems, is one of the basic information processes. To better understand communication and develop more efficient communication tools, it is important to have adequate and concise, static and dynamic, structured models of communication. The principal goal of this paper is explication of the communication structures, formation of their adequate mathematical models and description of their dynamic interaction. Exploring communication in the context of structures and structural dynamics, we utilize the most fundamental structure in mathematics, nature and cognition, which is called a named set or a fundamental triad because this structure has been useful in a variety of areas including networks and networking, physics, information theory, mathematics, logic, database theory and practice, artificial intelligence, mathematical linguistics, epistemology and methodology of science, to mention but a few. In this paper, we apply the theory of named sets (fundamental triads) for description and analysis of interpersonal communication. As a result, we explicate and describe of various structural regularities of communication, many of which are triadic by their nature allowing more advanced and efficient organization of interpersonal communication.
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Wang, Haibo, Naiqi Jiang, Ting Pan, Haiqing Si, Yao Li, and Wenjing Zou. "Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments." Journal of Advanced Transportation 2020 (November 12, 2020): 1–16. http://dx.doi.org/10.1155/2020/5640784.
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Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot’s cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety.
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Hordijk, Wim, and Mike Steel. "Autocatalytic Networks at the Basis of Life’s Origin and Organization." Life 8, no. 4 (December 8, 2018): 62. http://dx.doi.org/10.3390/life8040062.
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Life is more than the sum of its constituent molecules. Living systems depend on a particular chemical organization, i.e., the ways in which their constituent molecules interact and cooperate with each other through catalyzed chemical reactions. Several abstract models of minimal life, based on this idea of chemical organization and also in the context of the origin of life, were developed independently in the 1960s and 1970s. These models include hypercycles, chemotons, autopoietic systems, (M,R)-systems, and autocatalytic sets. We briefly compare these various models, and then focus more specifically on the concept of autocatalytic sets and their mathematical formalization, RAF theory. We argue that autocatalytic sets are a necessary (although not sufficient) condition for life-like behavior. We then elaborate on the suggestion that simple inorganic molecules like metals and minerals may have been the earliest catalysts in the formation of prebiotic autocatalytic sets, and how RAF theory may also be applied to systems beyond chemistry, such as ecology, economics, and cognition.
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Pissanetzky, Sergio, and Felix Lanzalaco. "Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence." Journal of Artificial General Intelligence 4, no. 3 (December 1, 2013): 10–43. http://dx.doi.org/10.2478/jagi-2013-0005.
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Abstract Awareness of the possible existence of a yet-unknown principle of Physics that explains cognition and intelligence does exist in several projects of emulation, simulation, and replication of the human brain currently under way. Brain simulation projects define their success partly in terms of the emergence of non-explicitly programmed biophysical signals such as self-oscillation and spreading cortical waves. We propose that a recently discovered theory of Physics known as Causal Mathematical Logic (CML) that links intelligence with causality and entropy and explains intelligent behavior from first principles, is the missing link. We further propose the theory as a roadway to understanding more complex biophysical signals, and to explain the set of intelligence principles. The new theory applies to information considered as an entity by itself. The theory proposes that any device that processes information and exhibits intelligence must satisfy certain theoretical conditions irrespective of the substrate where it is being processed. The substrate can be the human brain, a part of it, a worm’s brain, a motor protein that self-locomotes in response to its environment, a computer. Here, we propose to extend the causal theory to systems in Neuroscience, because of its ability to model complex systems without heuristic approximations, and to predict emerging signals of intelligence directly from the models. The theory predicts the existence of a large number of observables (or “signals”), all of which emerge and can be directly and mathematically calculated from non-explicitly programmed detailed causal models. This approach is aiming for a universal and predictive language for Neuroscience and AGI based on causality and entropy, detailed enough to describe the finest structures and signals of the brain, yet general enough to accommodate the versatility and wholeness of intelligence. Experiments are focused on a black-box as one of the devices described above of which both the input and the output are precisely known, but not the internal implementation. The same input is separately supplied to a causal virtual machine, and the calculated output is compared with the measured output. The virtual machine, described in a previous paper, is a computer implementation of CML, fixed for all experiments and unrelated to the device in the black box. If the two outputs are equivalent, then the experiment has quantitatively succeeded and conclusions can be drawn regarding details of the internal implementation of the device. Several small black-box experiments were successfully performed and demonstrated the emergence of non-explicitly programmed cognitive function in each case
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Chen, Qiuying, and Hongwei Mo. "A Brain-Inspired Goal-Oriented Robot Navigation System." Applied Sciences 9, no. 22 (November 14, 2019): 4869. http://dx.doi.org/10.3390/app9224869.
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Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability.
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Radanliev, Petar, David De Roure, Kevin Page, Max Van Kleek, Omar Santos, La’Treall Maddox, Pete Burnap, Eirini Anthi, and Carsten Maple. "Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars." Safety in Extreme Environments 2, no. 3 (October 2020): 219–30. http://dx.doi.org/10.1007/s42797-021-00025-1.
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AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.
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Butt, Muhammad A., Faisal Riaz, Yasir Mehmood, and Somyyia Akram. "REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles." SIMULATION 97, no. 9 (April 10, 2021): 601–17. http://dx.doi.org/10.1177/00375497211004721.
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Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.
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Baek, Sori, Amy L. Daitch, Pedro Pinheiro-Chagas, and Josef Parvizi. "Neuronal Population Responses in the Human Ventral Temporal and Lateral Parietal Cortex during Arithmetic Processing with Digits and Number Words." Journal of Cognitive Neuroscience 30, no. 9 (September 2018): 1315–22. http://dx.doi.org/10.1162/jocn_a_01296.
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Past research has identified anatomically specific sites within the posterior inferior temporal gyrus (PITG) and the intraparietal sulcus (IPS) areas that are engaged during arithmetic processing. Although a small region of the PITG (known as the number form area) is selectively engaged in the processing of numerals, its surrounding area is activated during both digit and number word processing. In eight participants with intracranial electrodes, we compared the timing and selectivity of electrophysiological responses in the number form area-surround and IPS regions during arithmetic processing with digits and number words. Our recordings revealed stronger electrophysiological responses in the high-frequency broadband range in both regions to digits than number words, with the difference that number words elicited delayed activity in the IPS but not PITG. Our findings of distinct profiles of responses in the PITG and the IPS to digits compared with number words provide novel information that is relevant to existing theoretical models of mathematical cognition.
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Miranda, R. C. R. "Identifying Conditions to Implement Strategic Knowledge Management in Brazilian Corporations — SKM Math Model Application." Journal of Information & Knowledge Management 08, no. 01 (March 2009): 67–77. http://dx.doi.org/10.1142/s021964920900221x.
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This is a step forward in researching about Strategic Knowledge Management — SKM. Firstly, an overview of SKM is presented and indicating the three integrative models: conceptual model, mathematical model and systemic model. After that, the SKM math model is deeply described in order to establish a framework to the study. The research objective was to evaluate conditions to implement SKM in Brazilian corporations. Thus, a methodology of four steps was applied: formulating a questionnaire, considering variables related to systemic factors — cognition, technology, organisational culture, managerial style and context — developing a Math Model Software, that was used to collect data and consolidating results by using a MS Excel; preparing a team of researches that comprised 29 students of the Intelligence Competitive MBA in the Universidade de Brasília. The research considered 15 companies in Brazil, mostly public ones, and 56 strategists and decision makers were heard. Results revealed that the conditions are unfavorable to implement SKM model and improvement actions on systemic factors are considerably required.
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Ferrari, Camilla, and Sandro Sorbi. "The complexity of Alzheimer’s disease: an evolving puzzle." Physiological Reviews 101, no. 3 (July 1, 2021): 1047–81. http://dx.doi.org/10.1152/physrev.00015.2020.
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The history of Alzheimer’s disease (AD) started in 1907, but we needed to wait until the end of the century to identify the components of pathological hallmarks and genetic subtypes and to formulate the first pathogenic hypothesis. Thanks to biomarkers and new technologies, the concept of AD then rapidly changed from a static view of an amnestic dementia of the presenium to a biological entity that could be clinically manifested as normal cognition or dementia of different types. What is clearly emerging from studies is that AD is heterogeneous in each aspect, such as amyloid composition, tau distribution, relation between amyloid and tau, clinical symptoms, and genetic background, and thus it is probably impossible to explain AD with a single pathological process. The scientific approach to AD suffers from chronological mismatches between clinical, pathological, and technological data, causing difficulty in conceiving diagnostic gold standards and in creating models for drug discovery and screening. A recent mathematical computer-based approach offers the opportunity to study AD in real life and to provide a new point of view and the final missing pieces of the AD puzzle.
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Eckstein, Shulamith G., and Michal Shemesh. "Mathematical models of cognitive development." British Journal of Mathematical and Statistical Psychology 45, no. 1 (May 1992): 1–18. http://dx.doi.org/10.1111/j.2044-8317.1992.tb00974.x.
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Wong, Pauline P., Georges Monette, and Neil I. Weiner. "Mathematical models of cognitive recovery." Brain Injury 15, no. 6 (June 1, 2001): 519–30. http://dx.doi.org/10.1080/02699050010005995.
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Pauline P. Wong, Georges Monette, N. "Mathematical models of cognitive recovery." Brain Injury 15, no. 6 (January 2001): 519–30. http://dx.doi.org/10.1080/02699050116774.
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Fujii, Keisuke. "Data-Driven Analysis for Understanding Team Sports Behaviors." Journal of Robotics and Mechatronics 33, no. 3 (June 20, 2021): 505–14. http://dx.doi.org/10.20965/jrm.2021.p0505.
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Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of behaviors in invasion team sports such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.
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Pinheiro-Chagas, Pedro, Amy Daitch, Josef Parvizi, and Stanislas Dehaene. "Brain Mechanisms of Arithmetic: A Crucial Role for Ventral Temporal Cortex." Journal of Cognitive Neuroscience 30, no. 12 (December 2018): 1757–72. http://dx.doi.org/10.1162/jocn_a_01319.
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Elementary arithmetic requires a complex interplay between several brain regions. The classical view, arising from fMRI, is that the intraparietal sulcus (IPS) and the superior parietal lobe (SPL) are the main hubs for arithmetic calculations. However, recent studies using intracranial electroencephalography have discovered a specific site, within the posterior inferior temporal cortex (pITG), that activates during visual perception of numerals, with widespread adjacent responses when numerals are used in calculation. Here, we reexamined the contribution of the IPS, SPL, and pITG to arithmetic by recording intracranial electroencephalography signals while participants solved addition problems. Behavioral results showed a classical problem size effect: RTs increased with the size of the operands. We then examined how high-frequency broadband (HFB) activity is modulated by problem size. As expected from previous fMRI findings, we showed that the total HFB activity in IPS and SPL sites increased with problem size. More surprisingly, pITG sites showed an initial burst of HFB activity that decreased as the operands got larger, yet with a constant integral over the whole trial, thus making these signals invisible to slow fMRI. Although parietal sites appear to have a more sustained function in arithmetic computations, the pITG may have a role of early identification of the problem difficulty, beyond merely digit recognition. Our results ask for a reevaluation of the current models of numerical cognition and reveal that the ventral temporal cortex contains regions specifically engaged in mathematical processing.
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Li, Wenjun, Lidong Tan, and Ciyun Lin. "Modeling driver behavior in the dilemma zone based on stochastic model predictive control." PLOS ONE 16, no. 2 (February 24, 2021): e0247453. http://dx.doi.org/10.1371/journal.pone.0247453.
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Driver behavior is considered one of the most important factors in the genesis of dilemma zones and the safety of driver-vehicle-environment systems. An accurate driver behavior model can improve the traffic signal control efficiency and decrease traffic accidents in signalized intersections. This paper uses a mathematical modeling method to study driver behavior in a dilemma zone based on stochastic model predictive control (SMPC), along with considering the dynamic characteristics of human cognition and execution, aiming to provide a feasible solution for modeling driver behavior more accurately and potentially improving the understanding of driver-vehicle-environment systems in dilemma zones. This paper explores the modeling framework of driver behavior, including the perception module, decision-making module, and operation module. The perception module is proposed to stimulate the ability to perceive uncertainty and select attention in the dilemma zone. An SMPC-based driver control modeling method is proposed to stimulate decision-making behavior in the dilemma zone. The operation module is proposed to stimulate the execution ability of the driver. Finally, CarSim, the well-known vehicle dynamics analysis software package, is used to verify the proposed models of this paper. The simulation results show that the SMPC-based driver behavior model can effectively and accurately reflect the vehicle motion and dynamics under driving in the dilemma zone.
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Labunskaya, V. A., and E. V. Kapitanova. "Self-Assessment and Appearance Evaluation in Student Group as Predictors in Relationships of Interpersonal Significance." Social Psychology and Society 7, no. 1 (2016): 72–87. http://dx.doi.org/10.17759/sps.2016070106.
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The paper attempts to integrate the concept of relationships of interpersonal significance, approaches to the correlation between physical appearance and life satisfaction, as well as several concepts of interpersonal cognition, self-assessment and evaluations of other people’s physical appearance. It introduces the concept of “insignificant/significant assessor of appearance” and argues that among the factors that turn a group member into the “insignificant/significant assessor of appearance” are evaluations, self-evaluations and group evaluations of physical appearance. The research described in the paper involved 89 students aged 19—21 (M=20 years), 66 females and 23 males, members of five groups that have been studying together for three years. The methods employed in the study included: “The Evaluation/Content Interpretation of Appearance and its Correspondence with Gender/Age Constructs”, a technique developed by V.A. Labunskaya; a modification of a sociometric test that helped reveal “insignificant/significant assessors of appearance”. Also, nonparametric mathematical methods were used to carry out comparative analysis. The outcomes show that there are considerable differences between the self-assessments, evaluations of physical appearance of those group members who are “significant assessors of appearance”, and group evaluations of their appearance. The research was conducted with the assistance of the Southern Federal University (project № 213.01-07-2014/15ПЧВГ “Threats to National Security in Situations of Geopolitical Competition and Models of Aggressive and Hostile Behavior in Children and Youth in Southern Russia” – project part of the inner grant of the Southern Federal University).
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Datsii, O., N. Datsii, O. Zborovska, L. Ivashova, M. Cherkashyna, and K. Ingram. "Financing of environmental programs for industrial waste management in times of crisis." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 1 (2021): 130–36. http://dx.doi.org/10.33271/nvngu/2021-1/130.
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Purpose. To conduct an analysis of funding from state and regional budgets for environmental needs in general and itemized as waste management, to identify correlations of data and to build on their basis a mathematical apparatus for forecasting the financing of environmental needs in the event of a budget crisis. Methodology. The results of the study were obtained using general and special methods of cognition. Methods of comparative analysis, content analysis and logical generalization were used to analyze the financing of environmental needs in general as well as waste management, in particular. Methods of quantitative and qualitative comparison were used to detect data correlation and isolation from random effects on the resulting function of non-random components. Methods of scientific abstraction and mathematical formalization were used to form a mathematical apparatus for forecasting the financing of environmental needs in the event of a budget crisis. Findings. It is proposed to introduce a strategic systems approach to address pressing issues of environmental protection and industrial waste management in the face of a shortage of financial resources both at the state level and at the regional level. The mathematical apparatus has been formed for this purpose. The increasing relevance of the forecast was achieved by introducing an original methodology. Trends and features of budget financing of ecological programs at the state and regional levels are revealed. With a chronic lack of financial resources, there is a tendency of a steady increase in current costs of waste management. Peculiarities of financing ecological needs from regional budgets are studied. It is stated that regional budgets are affected not only by the risks inherent in national funding, but also by their own sets of risks. Originality. The presence of correlated components in white noise of ARMA-models increased the relevance of forecasts of financing environmental programs in the crisis. The practical reliability of the correlation between some components of white noise and the integrated indicator of the level of economic security is established. It was found that the financing of environmental programs from regional budgets is characterized by more uneven changes than in the case of state funding. Practical value. Forecasts for the volume of industrial waste for disposal in specially designated places and the volume of capital investment and current costs of waste management have been developed. The possibilities of the mathematical model for the formation of forecasts of future periods are tested. Forecasts for the following years and approximations of previous periods are presented in a convenient analytical form to be used by specialists. Forecasting budget revenues for environmental needs allows planning a phased solution to environmental problems and attracting the necessary external financial resources, increases the ability of public control of financial flows and access to the planned indicators of each of the environmental investment objects. In a broader sense, it provides a tool for shaping the sphere of environmental protection as a single system.
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Khmara, D. S., and V. N. Samotuga. "Development of E-Commerce in the Field of Small and Medium Enterprises." Economics and Management 27, no. 6 (July 23, 2021): 426–36. http://dx.doi.org/10.35854/1998-1627-2021-6-426-436.
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Aim. The presented study aims to produce a set of tools for modeling the development of internet companies.Tasks. The authors show the competitive advantages of small and medium enterprises; identify the main features of the modern business environment; determine the structure of modern e-business; identify Internet factors affecting a company that implements Internet technologies in its commercial activities; identify the main factors that make the Internet market attractive for small and medium enterprises and determine their readiness to implement e-business; provide an overview of government support for small and medium enterprises in the context of a pandemic.Methods. This study uses general scientific methods of cognition to analyze the problems of e-business development among small and medium enterprises in Russia.Results. The development of e-business has produced new functions and business processes, adding fundamentally new qualities to the companies’ commercial activities. The ultimate goal of modeling e-business development is to create a set of tools for the formation of an efficient development program for Internet companies that involves building adaptive economic and mathematical models for managing the development of an Internet company as a complex system.Conclusions. The formation and rapid expansion of the national market of computer technologies and software, the growing number of Internet users, and the creation of electronic payment systems in Russia facilitate the introduction and spread of e-commerce among small and medium enterprises. At the same time, doing business online involves solving many problems under conditions of risk, uncertainty, and certain restrictions.
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O'Donnell, Timothy J., Marc D. Hauser, and W. Tecumseh Fitch. "Using mathematical models of language experimentally." Trends in Cognitive Sciences 9, no. 6 (June 2005): 284–89. http://dx.doi.org/10.1016/j.tics.2005.04.011.
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Lamberts, Koen. "Array Models of Cognition." Journal of Mathematical Psychology 40, no. 3 (September 1996): 271–74. http://dx.doi.org/10.1006/jmps.1996.0025.
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Elliott, T., C. I. Howarth, and N. R. Shadbolt. "Axonal Processes and Neural Plasticity: A Reply." Neural Computation 10, no. 3 (April 1, 1998): 549–54. http://dx.doi.org/10.1162/089976698300017656.
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We examine the claim that a class of sprouting-and-retraction models is mathematically equivalent to a fixed-anatomy model. We accept, subject to important caveats, a narrow mathematical equivalence of the energy functions in both classes of model. We argue that this narrow equivalence of energy functions does not, however, entail equivalence of the models. Indeed, the claim of complete model equivalence hides significant dynamical differences between the approaches, which we discuss. We also disagree that our work demonstrates that subtractive constraint enforcement is natural in fixed-anatomy models.
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Zak, Michail. "Physical models of cognition." International Journal of Theoretical Physics 33, no. 5 (May 1994): 1113–61. http://dx.doi.org/10.1007/bf01882756.
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